Abstract We propose to develop a novel computational framework describing single cell gene expression to aid model building and design for synthetic biology applications. Currently there are three major challenges to this goal. First, there is a knowledge gap between experimental measurements and mathematical models: experiments on synthetic circuits typically provide partial information, following the expression of a few proteins using fluorescent tags while leaving many other molecular network components (such as promoters, protein-protein complexes) uncharacterized. By contrast, existing theoretical models make ad-hoc assumptions regarding the network and its interactions ? more information than experiments can provide ? and about noise statistics, thus leading to over-parameterization. Next, over-parameterization is also a problem for circuit design that demands models with minimal set of parameters to efficiently search through the parameter space. Third, experimental data is in fluorescence and not in protein numbers, rendering traditional models inapplicable. The lack of models that predict single cell level behavior hinders our basic understanding of these circuits and ability to manipulate cellular heterogeneity to control microbial dynamics. We propose to bridge this gap. An important breakthrough lies in realizing that stochastic time courses of protein expression in single cells hold crucial information about network details that are not directly visible otherwise. We have built a novel mathematical tool called MaxCal, capable of harnessing information hidden in the noisy protein expression trajectories to infer underlying models of synthetic circuits. Moreover, MaxCal works directly with trajectory and hence easily incorporates additional algorithms to convert fluorescence to protein number (FNC) and avoids data reduction unlike other methods. MaxCal is a top-down approach that builds the minimal model, avoids the over-fitting issues of traditional approaches, and yields an effective feedback parameter facilitating design. We will use this novel integrated framework (MaxCal + FNC) to build models for high quality single cell temporal data that are becoming available with novel microfluidics tools. This is different from large-scale network and transcriptome wide measurement pooling data over many cells. We will analyze raw protein expression (recorded in fluorescence) trajectory data in specific S. cerevisiae strains containing a synthetic positive feedback network that behaves as a biological switch, controlled by an inducer. This network is bimodal, with cells dynamically switching between high and low expression levels of the target protein, a strategy called bet-hedging frequently used by microbes that evade treatment by antibiotics. This application may have potential therapeutic relevance in dealing with microbial populations becoming resistant to antibiotics and other stressors. We will also address several design questions to build new circuits for specific function.